Dive into end-to-end Machine Learning Operations (MLOps) with Vertex AI. Discover how to integrate data, models, and workflows to build scalable, reliable ML pipelines. Learn how Vertex AI automates key ML lifecycle stages like training, hyperparameter tuning, deployment, and monitoring. Whether you're starting out or optimizing workflows, this session covers best practices and real-world use cases to transform your approach to AI development. Perfect for data scientists and ML engineers!

Faster AI innovation cycles often require MLOps – a critical, but complex, undertaking. Join us in this session to discover how a platform approach to MLOps on Vertex AI simplifies and accelerates the entire AI life cycle. Learn how to streamline development, deployment, and management of your AI models. We’ll also share real-world success stories from customers like GSK.

Discover how to break free from the cycle of endless AI Proofs of Concept (POC) and unlock scalable, enterprise-wide impact. In this session, we’ll explore proven strategies for operationalizing AI, including leveraging cloud-native solutions like Vertex AI, building robust MLOps pipelines, and defining measurable ROI tied to business goals. Through real-world examples and actionable insights, learn how to overcome common scaling challenges, drive cultural adoption, and future-proof your AI strategy for sustained innovation and success.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Join us for a lightning talk summarizing the Google x Kaggle Gen AI Intensive, a 5-day live course that empowered over 140,000 participants with a comprehensive understanding of generative AI. From foundational models and prompt engineering to MLOps and real-world applications, this series covered it all through a mix of theory, hands-on learning, and community engagement, with learning material created by experts across Google. Learn how you can leverage the resources from this ongoing series to upskill yourself and stay ahead in the rapidly evolving field of generative AI.

Boost AI innovation in regulated industries! Use Red Hat OpenShift AI/IBM Watsonx and Vertex AI to split model lifecycles: train securely on isolated OpenShift AI/Watsonx, then deploy via Vertex AI's model registry. Explore use cases, deployment patterns, and the advantages of this approach. Ensure training on a trusted platform for sensitive data. Deploy models easily via Google Cloud's registry. Learn how this MLOps architecture helps meet regulations while leveraging cloud AI.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Discover how Target modernized its MLOps workflows using Ray and Vertex AI to build scalable ML applications.  This session will cover key strategies for optimizing model performance, ensuring security and compliance, and fostering collaboration between data science and platform teams. Whether you’re looking to streamline model deployment, enhance data access, or improve infrastructure management in a hybrid setup, this session provides practical insights and guidance for integrating Ray and Vertex AI into your MLOps roadmap.

Automate AI deployment and management. Build efficient machine learning operations (MLOps) pipelines with Vertex AI.